Language Varieties of Italy: Technology Challenges and Opportunities

Language is a primary means for communication that is intrinsic to the expression of culture. Through languages, we signal our social identities and convey part of our heritage (Thomason, 2015). However, according to the UNESCO Atlas of World’s Languages in Danger (Moseley, 2010) about half of the spoken languages in the world are at risk of disappearing by the end of the century. Ultimately, this will lead to a loss of an integral part of cultures and traditions (Hale et al., 1992).

The natural language processing (NLP) community has recently started to include endangered languages in its repertoire, and language varieties of Italy are no exception. However, most of the efforts in NLP implicitly assume that these language varieties are just under-resourced entities (in terms of written data availability) with an established written form, and with the same functions and technological needs of high-resource standardized languages with institutional support, such as Italian or English (Bird, 2022). This machine-centric approach not only fails to acknowledge that most endangered languages are primarily oral, without a standardized orthography and canonical variant, often code-switched with a co-territorial “high-prestige” standardized language, and serving different language functions to other languages within the local linguistic ecosystem (Fishman, 2001), but also disregards what and how technologies should be built to safeguard endangered languages in the interest of speech communities (Bird, 2020; Caselli et al., 2021).

In this paper, we discuss the technology challenges and opportunities for language varieties of Italy, one of the most linguistically diverse landscapes in Europe which, according to UNESCO (Moseley, 2010), currently counts over 30 languages in danger. Italy’s languages and dialects are not only many for such a small area (Maiden and Parry, 1997), but they are also very different from each other, and their linguistic distance does not typically relate to geographical distance (Avolio, 2009). Most of these varieties are Romance, albeit Germanic, Slavic, Albanian, and Hellenic ones also shape the Italian linguistic landscape. As for the majority of endangered languages, most of Italy’s language varieties comprise many local variants, have no standardized written form, and are just occasionally written, insofar as they are primarily used in spoken, informal settings. They typically exist in a peculiar diglossic situation with Italian, and vary in terms of recognition, protection, economic incentives, and prospects.

After introducing the linguistic situation in Italy (Section 2), we review efforts in NLP for its languages and dialects (Section 3). We then discuss the machine-centric assumptions of the default NLP approach when dealing with these varieties, namely, the exaggerated focus on “machine-readable” written data, the little regard for the representativeness of such materials of speech communities, and the homogeneous view of functions, uses, and needs across language varieties (Section 4). We argue that language varieties of Italy should not be approached as a data commodity for machine learning advances, and that technology should serve language varieties and their speakers and not the other way round. We thus present recommendations and opportunities for speaker-centric NLP and advocate for a local community aimed at responsibly supporting vitality of Italy’s varieties through sensitization on ethical engagement, sharing of practices, participatory collaboration, and active awareness-raising (Section 5). Finally, we provide our conclusions (Section 6).

We i) expose the NLP community to endangered language varieties of Italy, ii) survey computational work for these varieties, and iii) shed light on the main assumptions and shortcomings of the standard machine-centric NLP approach. iv) We then identify directions and opportunities for responsible, speaker-centric efforts aimed at preserving language varieties of Italy. Finally, v) we call for a local, multidisciplinary community that supports participatory work and knowledge sharing towards common goals. We hope our recommendations will be useful for the safeguarding of other endangered languages, too.

Italy is one of the most diverse landscapes in Europe in terms of language varieties (Avolio, 2009). Unified late, the country was previously a collection of states with their own local languages. After the political unification in 1861, Standard Italian (ISO 639-3 code: ita) was adopted by the state as the official language, making it a unifying element. Italian emerged from a literary language based on Vulgar Latin, and specifically from the Tuscan variety as spoken by the Florentine upper-class society (Maiden and Parry, 1997). At unification time, Italian was spoken by less than 10% of the population (De Mauro, 1963), and rates of literacy remained low for over a century, especially in rural areas. Along with education, the rise of mass media played a crucial role in establishing the widespread use of Standard Italian, mirrored by a substantial decline in the use of local languages.¹ Nowadays, Italian is the fourth most widely spoken Romance language in the world with about 68M speakers (Eberhard et al., 2022).

Despite the establishment of Italian as national language, many local languages and dialects are still currently spoken in Italy. In Table 1 we report the language varieties of Italy classified as endangered by UNESCO (Moseley, 2010) along with their ISO 639-3 code (wherever available), linguistic branch, level of endangerment, number of speakers, and whether they have a standardized written form.

While most varieties have fewer than 1M speakers and are definitely or severely endangered, some are still used even by younger generations in informal settings, i.e., language varieties spoken in the south and northeast areas of the Italian peninsula (ISTAT, 2017). Just like most languages of the world, languages and dialects of Italy are primarily used in spoken contexts, and only a fraction of them have a recently established written form. Most language varieties of Italy are Romance, insofar as they locally evolved from Vulgar Latin like Standard Italian.² The rest include non-Latin linguistic minorities from Germanic, Albanian, Hellenic, and Slavic Indo-European branches.

Due to the complex historical and sociopolitical motivations behind the use—with negative connotation—of the term dialetti (“dialects”) for language varieties of Italy (Avolio, 2009), and the range of meanings that the term assumes according to the context in which it is situated (Berruto, 2005), we hereafter refer to those languages and dialects as language varieties.³ In the following, we contextualize endangered language varieties of Italy (Table 1) within the linguistic macro-areas proposed in the renowned Carta dei dialetti d’Italia (Pellegrini, 1977). An indicative linguistic map is also shown in Figure 1. For more details on the features of each variety and a systematic characterization of them, including local variants, we refer the reader to relevant linguistic studies and overviews on the topic (Pellegrini, 1977; Maiden and Parry, 1997; Avolio, 2009, inter alia).

This includes Gallo-Italic varieties situated in northern Italy (i.e., Piedmontese, Ligurian, Lombard, Emilian, Romagnol) and Venetian, along with their many local variants.

Friulian, a Rhaeto-Romance language recognized by the Italian state and spoken in northeast Italy, along with its local variants.

Non-endangered language varieties that are closely related to Standard Italian (middle-northern Italy; Figure 1, horizontal lines).

Non-endangered varieties in central Italy (Figure 1, dots), intermediate-southern varieties (e.g., Neapolitan as a group of closely related varieties spoken in southern continental Italy), and extreme-southern varieties (i.e., Sicilian, its local variants, and related varieties).

Varieties spoken in the island of Sardinia. These include the officially recognized Sardinian macro-language (comprising Logudorese and Campidanese) and Gallurese and Sassarese, spoken in the north of the island.

These include protected varieties such as Francoprovençal as spoken in the Aosta Valley and Piedmont and the Vivaro-Alpine Occitan variety (all in northwest Italy), the Rhaeto-Romance Ladin language (northeast Italy), the Austro-Bavarian South Tyrolean variety (northern Italy), and Slovenian varieties (northeast Italy; Figure 1, vertical lines), including Resian. Varieties of Judeo-Italian are also spoken across the country by very small Jewish communities.

A number of language islands enrich the already complex linguistic landscape of Italy (Figure 1, black dots). These include Germanic varieties in northern Italy (i.e., Cimbrian, Mòcheno, Walser, Töitschu); Modern Greek varieties in the Salento and Calabria areas, southern Italy (i.e., Griko and Calabrian Greek); the Molise Slavic Serbo-Croatian variety in the Molise region, middle-southern Italy; the Francoprovençal Faetar variety spoken in two small towns in Apulia and the Vivaro-Alpine Gardiol enclave in the Calabria region, southern Italy; the Gallo-Italic of Sicily Lombard enclave in the island of Sicily; the Algherese Catalan variant spoken in Alghero (Sardinia); and Arbëreshë Albanian, whose communities are scattered across southern Italy (Figure 1, white stars).

Alongside Italian and indigenous language varieties and linguistic minorities, regional varieties of Standard Italian (hereafter, regional Italian) are also spoken by most Italian speakers. Varieties of regional Italian result from a geographical differentiation of Standard Italian after its widespread adoption, and differ from each other at various levels, i.e., syntax, morphology, phonetics, phonology, and prosody (Cerruti, 2011; Avolio, 2009). The various forms of regional Italian mostly match macro-linguistic areas of language varieties (cf. Section 2.2), and vary according to social and educational factors (Avolio, 2009).

The study, preservation, and promotion of language diversity have recently gained increasing attention in the NLP community. Initiatives such as the ACL 2022 special theme on “Language Diversity: From Low-resource to Endangered Languages” (Muresan et al., 2022), the ACL special interest group SIGEL,⁴ and relevant workshops, e.g., ComputEL (Harrigan et al., 2023), Eurali (Ojha et al., 2022), AmericasNLP (Mager et al., 2021), have been proposed. Moreover, the VarDial series of workshops (Scherrer et al., 2023, inter alia) is being routinely organized to promote the study of diatopic variation of language varieties and dialects.

In the following, we review previous work in NLP for Italy’s varieties, from monolingual (Section 3.1) to multilingual efforts (Section 3.2), and highlight commonalities and differences in the shortcomings of both research lines (Section 3.3).

Natural language processing research for specific languages and dialects of Italy is scarce and scattered across disciplines. The most studied language variety is Venetian, for which there exist work on morphological analysis (Tonelli et al., 2010), part-of-speech tagging (POS; Jaber et al., 2011), word sense disambiguation (Conforti and Fraser, 2017), and a preliminary investigation on Venetian-English machine translation (MT; Delmonte et al., 2009). Ligurian has also recently gained attention in NLP, with work on text normalization (Lusito et al., 2023) and the development of a Universal Dependency (UD; de Marneffe et al., 2021) treebank for the Genoese variety (Lusito and Maillard, 2021). A small set of Vivaro-alpine examples has been included in an Occitan subcorpus with POS annotations (Bernhard et al., 2018, 2021), whereas for Ladin, previous work includes MT from and to Italian for the Val Badia variety (Frontull, 2022). MT has also been studied for Sicilian$⇆$ English and zero-shot Sicilian$⇆$ Italian (Wdowiak, 2022), and for Italian→Sardinian (Tyers et al., 2017) and Catalan→Sardinian (Fronteddu et al., 2017).

Among severely endangered varieties, Griko is the most represented in NLP. Previous work includes two Griko-Italian parallel corpora: A corpus of narratives with POS annotations (Anastasopoulos et al., 2018; Chaudhary et al., 2021) and a small speech-derived corpus annotated with morphosyntactic, POS, glosses, and speech-related information (Boito et al., 2018; Lekakou et al., 2013). Other efforts in this space include Molise Slavic, for which field recordings, transcriptions, and Italian and German translations have been made available for the varieties of Acquaviva Collecroce, San Felice, and Montemitro (Breu, 2017).

A number of resources have been produced for plurilingualism areas of Italy where South Tyrolean is spoken, such as a multilingual corpus of computer-mediated communication (Frey et al., 2016), and a longitudinal trilingual corpus of young learners (Glaznieks et al., 2022). Preliminary efforts such as a morphosyntactic specification for Resian (Erjavec, 2017), a lexical database for Sardinian, Gallurese and Sassarese (Angioni et al., 2018), and a tagset for Cimbrian varieties (Agosti et al., 2012) have also been carried out. There also exist a few cultural institutes that have developed tools and resources that can be interrogated online, e.g., Micurá de Rü⁵ (Ladin), and Kulturinstitut Lusérn⁶ (Cimbrian), inter alia.

Language varieties of Italy are increasingly represented in multilingual research. Friulian, Ladin, Neapolitan, and Venetian have been included in the Sigmorphon shared tasks on morphological inflection in 2018–2020 (Cotterell et al., 2018; McCarthy et al., 2019; Vylomova et al., 2020), though the latter two have been discontinuously represented. More recently, a language and dialect identification shared task has been proposed (Aepli et al., 2022), for which participants were given Wikipedia dumps of 11 varieties of Italy and were asked to classify text samples for a subset of the given varieties. Friulian, Ligurian, Lombard, Sicilian, Sardinian, and Venetian have also been included in a translation model covering 202 languages (NLLB Team et al., 2022), and a corpus for cross-lingual spoken language understanding has been annotated with slot and intent information in South Tyrolean and Neapolitan (van der Goot et al., 2021b; Aepli et al., 2023).

Other efforts including language varieties of Italy are sparse and mainly focus on learning methods, e.g., for learning contextualized cross-lingual word embeddings in low-resource scenarios (Griko in Wada et al., 2021), for language identification of text sequences in mixed-language documents (Lombard-English in King and Abney, 2013), or for investigating the effect of pretraining language selection on downstream zero-shot transfer (Piedmontese in Malkin et al., 2022).

Multilingual pretrained language models have also been proposed in recent times to widen language coverage in NLP, e.g., mBERT (Devlin et al., 2019), mBART (Liu et al., 2020), and XLM-R (Conneau et al., 2020). mBERT includes some of Italy’s varieties, namely, Lombard, Piedmontese and Sicilian, albeit under-represented in terms of pretraining data compared to other languages. Training material is taken from entire Wikipedia editions, regardless of the covered variants, quality issues, and the representativeness of such language of speech communities (cf. Section 4.2).

From a closer look, we can observe that the attention to local language varieties and the very objectives of research efforts generally diverge between monolingual and multilingual NLP studies. Most efforts for specific language varieties of Italy are explicitly intended to study or support local languages and dialects, state the orthographies and local variants being considered, and are often conducted by members of the target speech communities—and are thus potentially driven by actual or perceived needs. On the other hand, recent trends in multilingual NLP are typically centered on computational advances (e.g., scaling, generalizing) rather than on varieties and their speakers. Indeed, most work in this space is driven by language technology agendas of standardized languages (Bird, 2022), and view the under-resourcedness of written content as a pivotal problem to be directly or indirectly fixed, do not mention which variants and orthographies of the language varieties have been included—and why they have been chosen over the others—perpetuating language monolithicity assumptions, and implicitly presume that language varieties are all the same in terms of functions, uses, and their speakers’ needs (Section 4).

What both research strands have in common is that the active involvement of speech communities at various stages of the design process (e.g., to express needs or assess the envisioned technology rather than merely acting as data producers) is typically left unspecified. This confirms similar findings by Caselli et al. (2021) and motivates us to propose new ways of working centered on language varieties and their communities (Section 5).

In this section, we provide an in-depth overview of the main assumptions and shortcomings of the default NLP approach—what we refer to as machine-centric NLP—with a focus on language varieties of Italy. We first discuss the persistent focus on written data scarcity and how this is often perceived as a problem to be solved (Section 4.1). Second, we focus on widespread text collections that are typically used for training language models, arguing that the common practice of language data as a commodity fails to represent language varieties and their speakers (Section 4.2). Lastly, we discuss the intrinsic assumptions of the standard approach, namely the lack of regard for functions, contexts and needs of varieties (Section 4.3).

A common argument in NLP work involving local language varieties of Italy is that these languages and dialects are under-resourced in terms of “machine-readable” written data, and are therefore in need of more resources—or computational means to bridge the gap—in order to take full advantage of language technologies. This view not only fails to acknowledge the reasons behind written data scarcity, but also implicitly homogenizes the contexts in which such language varieties are situated and the diverse aspirations for written text—and thus the needs for consequent technologies.

The focus on “data quantity” is widely rooted in the NLP community, and the amount of machine-readable language resources has also recently been used as a criterion for classifying the world’s languages and highlighting their technological disparity. For instance, in the taxonomy of world’s languages according to data availability by Joshi et al. (2020), 10 endangered varieties of Italy are in the second-worst position (1: The Scraping-bys), while the rest belong to the worst position (0: The Left-behinds).⁷ Despite the best of intentions, this classification decouples the unique situation of each variety from its volume of machine-readable resources. While the amount of written data resources and the position in the “technologization race” are probably of interest to standardized and “would-be standardized languages”⁸ (see Bird, 2022), these factors are of little significance for most varieties, for which interests typically relate to culture preservation, language learning, and intergenerational transmission (Section 5).

Given the varied linguistic and socio-political contexts of Italy’s varieties (Section 4.3), it is therefore more appropriate to outline which these resources are rather than how many they are. Hence, we extended the search by Joshi et al. (2020), which originally included the LDC catalog,⁹ ELRA Map,¹⁰ and Wikipedia, by covering additional repositories and all Italy’s varieties. We searched for main and alternate names of each variety (e.g., nap: Neapolitan, Neapolitan-Calabrese, Continental Southern Italian) on OLAC (Simons and Bird, 2003), the CLARIN Virtual Language Observatory (Hinrichs and Krauwer, 2014), and OPUS (Tiedemann, 2012). The latter also includes data from educational resources, e.g., Tatoeba,¹¹ QED (Abdelali et al., 2014) and localization data from open-source software projects e.g., Ubuntu, Gnome, Mozilla. To further include language resources that have not been submitted to mainstream repositories, we also queried Google Scholar for publications that mention both NLP and a main or alternate name of a variety. We thoroughly inspected the top 50 results for each query and retained all entries that present or use language resources. Finally, we categorized all publicly available, curated language resources according to their language varieties, text genres, annotation types (if any), languages of parallel data (if applicable), and dataset size (Table 2). Moreover, we inspected Wikisource, Project Gutenberg,¹² UDHR,¹³ and raw corpora typically used in multilingual research for the presence of Italy’s varieties (Table 3).

Curated corpora for language varieties of Italy (Table 2) greatly vary in terms of objectives, from language documentation (Boito et al., 2018; Breu, 2017) to supporting multilingual information access (Aepli et al., 2023; NLLB Team et al., 2022; van der Goot et al., 2021b). They cover a handful of language varieties (and variants, cf. Section 3.1), are sparse in terms of text genres and annotation types, and are generally small in size. But (for what) is this a problem? Both scarcity and sparsity of written content are a challenge to researchers embracing a machine-centric view, who may be tempted to uniformly scale current language technologies to these varieties by creating or crowd-sourcing new written corpora with annotations for a variety of tasks, design “data-efficient” or zero-shot methods to bridge the data scarcity gap, or just build upon raw corpora (Table 3) such as web-crawled text collections regardless of how representative the content and subsequent technologies are of language varieties and speech communities (Section 4.2). Language technologists should here take a step back and thoughtfully reflect on why there is a lack of machine-readable written resources for Italy’s varieties and whether this is relevant to the target speech communities. By detaching from the machine-centric view of technology and engaging with speakers of local language varieties, one can realize that most languages and dialects of Italy are primarily oral, have different aspirations for written content and text-based technologies, vary in prospects according to the linguistic and socio-political contexts in which they are embedded, and serve different functions than standardized languages (Section 4.3). Indeed, with the exception of a few language varieties that benefit from protection, economic incentives, or co-official status with Italian, and for which a written form is used or envisioned for official purposes (Section 5), written data is likely to remain scarce.

Another assumption of the machine-centric NLP approach is that, if there is any text collection for a given language variety, it is homogeneous, representative of the community of speakers, and free of noise and boilerplate content, and therefore can be directly used for representing that language variety in language technology. However, unlike the case of standardized languages, most text collections for endangered languages naturally include content in multiple variants, freely written following no consistent or widely established orthography (e.g., Lombard Wikipedia (Miola, 2017)), or comprise a large amount of wrong language and non-linguistic materials (Kreutzer et al., 2022). Nevertheless, those resources are typically taken monolithically regardless of their actual content. In this section, we take Wikipedia and multilingual web-crawled corpora as case studies of mainstream text collections which are used in current NLP regardless of their representativeness of language varieties and speech communities.

Wikipedia is by far the most widely used resource in NLP when it comes to the so-called under-resourced languages. It currently comprises content in 320 languages (as of 2023-09-10), of which 10 are endangered varieties of Italy. It additionally includes two more Wikipedias (i.e., eml, roa-tar) with deprecated or arbitrary language codes. Despite the role of Wikipedia on preserving knowledge even in lesser-used languages, the written content for most endangered varieties has to be taken carefully with regards to the varied guidelines among projects and the potential presence of fictitious and culturally-biased content. For instance, the Lombard Wikipedia leaves users freedom with respect to orthography and local variants (provided that they indicate these on the article page) (Miola, 2017), whereas the written content on the Piedmontese edition of Wikipedia does not match any variety actually spoken (Miola, 2013). A varied use of orthography and local variants can be observed in other Wikipedia editions for Italy’s varieties, such as the Ligurian Wikipedia (Lusito and Maillard, 2021). More broadly, the content of small Wikipedias typically comprises translations of pages from larger editions (e.g., English) rather than including original content tied to speakers’ identity (Gobbo and Miola, 2016). Besides objectivity, this has the effect to homogenize cultures and perspectives (Callahan and Herring, 2011).

Near-duplicate articles are also common in Wikipedia editions for Italy’s varieties. For instance, the Venetian edition of Wikipedia (∼69K pages) contains placeholder content for years from 1 BC to 999 BC (1K pages) and for most of the days of the year, as well as template articles for many municipalities and provinces around the world. This suggests that a relevant portion of the encyclopedia could be generated by bots, and thus that Wikipedia texts for Italy’s language varieties not only reflect a rather artificial use of language—what we tentatively call wikivariety—but also that the actual content is less than one might think.

Lastly, while eml has been deprecated more than 14 years ago¹⁴ in favour of egl and rgn as separate ethnolinguistic entities (Maiden and Parry, 1997), it is still in use on Wikipedia. Most eml pages indicate the specific variety at the top of the article, but this is rarely considered in NLP, where whole Wikipedia editions are taken as monolithic entities for training language models.

The presence of Italy’s varieties on Wikipedia has an impact on the creation of web-crawled datasets. It is not surprising that multilingual corpora that include those varieties are the ones that rely on fastText LangID (Joulin et al., 2017), a language identification model that currently includes a handful of Italy’s language varieties and whose training material is mostly taken from Wikipedia.

Following Kreutzer et al. (2022), who have recently highlighted systematic issues with web-crawled dataset portions for “low-resource languages”, we manually audit the content of crawled corpora which include Italy’s varieties (cf. Table 4) and are easily accessible. The resulting datasets are CCAligned (El-Kishky et al., 2020), WikiMatrix (Schwenk et al., 2021) (parallel), and OSCAR (Abadji et al., 2022) (monolingual).¹⁵

For each corpus, a native speaker of each included language variety was asked to label a random sample of 50 texts (or parallel texts, in CCAligned and WikiMatrix) according to the labeling scheme and guidelines presented in Kreutzer et al. (2022).¹⁶ Possible labels are c_nat (correct, natural), c_sho (correct, short), c_boi (correct, boilerplate), w_tra (wrong translation – applicable to parallel corpora only), w_lan (wrong language), and w_nlg (wrong, not language). For lmo and scn in OSCAR, the total instances are less than 50, and thus all of them have been audited. Compared to Kreutzer et al. (2022), we audit data from the latest OSCAR version (22.01), whereas for CCAligned and WikiMatrix we contribute to new language pairs (i.e., en-srd and it-scn, respectively) and report their results for en-lmo on WikiMatrix since we use the same data release and labeling scheme. To go beyond the approach of viewing language varieties as monoliths, native speakers were also asked to mark instances whose variants are hard to categorize because they exhibit traits of continuity with multiple varieties.¹⁷

We present the results of the audit in Table 4. For each corpus and language variety, we also report the number of texts and the percentage of samples we audited. Moreover, we indicate the percentage of Wikipedia content on audited subsets. OSCAR is the corpus with the highest ratio of “correct”¹⁸ content (from 50% to 100%); however, very few instances are included in most subsets (e.g., 2 for lmo and scn), and thus results have to be taken with a grain of salt. On the contrary, the previous OSCAR version had more instances, including additional language varieties (Abadji et al., 2022), but the actual linguistic content for most of those was dramatically low, e.g., 0.0% in-language samples for nap (Kreutzer et al., 2022). Interestingly, the sample marked as “wrong language” in the lmo subset comes from the eml Wikipedia edition where it is labeled as Piacentino, a variant of egl which exhibits traits of continuity with lmo. This suggests that discretizing variants into bounded languages is rather limiting since they lie on a continuum.

Regarding parallel corpora, most of the content for srd on CCAligned is in another language (30%), a wrong translation (28%), or do not even contain linguistic content (10%). Among the 32% “correct” samples, just one instance (2%) has a clean content. The remaining 30% contain website headers, footers, and other boilerplate content. The situation is even worse on WikiMatrix: While parallel texts are cleaner than CCAligned, most pairs are not translations of each other (81.4% on lmo, 78.0% on scn). The ratio of “correct” content is thus quite low, ranging from 12.8% to 16.0%.

Overall, aside from the domain-specific WikiMatrix, we observe that most of the in-language material for Italy’s language varieties comes from Wikipedia articles. This suggests that content that is not already included in other resources is rarely captured, both because language identifiers trained on Wikipedia are likely to leave nothing but wikivarieties, and most importantly because Italy’s varieties are rarely written down, and if so, they are mostly code-switched with a co-territorial “high-prestige” standardized language with vehicular functions, e.g., Italian (Section 4.3).

To conclude, we argue that viewing Italy’s varieties as a data commodity for machine learning purposes without asking whether the linguistic content is representative of speech communities disregards the nature of language varieties and ignores their speakers. We encourage researchers to care about the varieties they work with and responsibly engage with speech communities (Section 5).

The strongest assumption of the machine-centric approach is arguably to consider the diverse functions, contexts, and needs of language varieties as homogeneous—and typically in the image of high-resource standardized languages, e.g., Italian or English. This practice has the effect to reduce language varieties to mere linguistic codes that are dissociated from their distinctive situations.

By looking at the contemporary sociolinguistic context of Italy, most local language varieties exist in a situation of dilalìa (Berruto, 1987) with the national language. While Italian serves as the “high-prestige” vehicular language (Fishman, 2001), and it is therefore the language used in all formal settings (i.e., from education to administration), Italy’s languages and dialects are primarily confined to spoken, informal situations (e.g., family, local participation), and Italian functionally overlaps with them in those informal domains—making the situation different from the rigidly compartmentalized diglossia (Avolio, 2009). Exceptions are language varieties within territories in which bilingualism is officially granted by national laws, i.e., those of the German minority in the South Tyrol province (northern Italy), the French minority in the Aosta Valley (northwest Italy), and the Slovenian minority in some municipalities of the Friuli-Venezia Giulia region (northeast Italy). In those cases, local language varieties typically enjoy the same standing of the national language and are used (or are aimed to be used) to serve “high-prestige” functions. This functional differentiation should be the starting point for language technologists to reflect on the (often considered homogeneous) utility of text-based language technologies across language varieties.

The socio-political contexts in which language varieties are situated have an impact on language vitality prospects and community aspirations, too. For instance, some language varieties and their culture are protected by the Italian Law 482/1999 (1999),¹⁹ albeit safeguarding measures differ on how they are locally implemented. Moreover, some of them also benefit from recognition and safeguard by regional laws,²⁰ or are even locally co-official (i.e., German and Ladin in the South Tyrol province and French in the Aosta Valley). Finally, some language varieties are solely recognized or promoted locally, or both.²¹ These diverse situations must be attentively considered, and engaging with local communities would allow the researcher to deeply understand how this affects their ambitions and needs (Section 5).

As regards written use, although some language varieties of Italy have a notable literary tradition (Avolio, 2009) (e.g., works in Venetian by C. Goldoni [18th century] and in Neapolitan by G. Basile [16th century], inter alia), we stress that they are nowadays primarily used in spoken, informal settings, and most of them have no standardized written form. Even if official orthography standards exist for some varieties, these are often unknown to speakers themselves. Indeed, in our experience speakers write “the way words sound” in their local variants, using just the available characters in their keyboards. Normalizing user-generated texts to a “standard” form (e.g., Baldwin et al., 2015; van der Goot et al., 2020, 2021a) has proven useful for NLP purposes, but it inevitably erases the naturally occurring sociolinguistic variation (Nguyen et al., 2021), homogenizing all variants of a language variety and imposing a “correct” form of writing.

But how often do speakers write in their own variety? With the exception of restricted communities on social media and few dedicated websites, writing in some of Italy’s language varieties is rather uncommon. Code-switching—the alternation of different language varieties in a single discourse—is instead a more widespread practice in Italy (Cerruti and Regis, 2005), where Standard Italian—or any co-territorial “high-prestige” language in border areas—is mixed with both Italy’s varieties and regional Italian. This brings into question the utility of sentence- and document-level language identification tools supporting Italy’s language varieties.

The assumptions and shortcomings discussed in the preceding sections make evident that the current machine-centric approach neither respects nor represents language varieties of Italy and their speakers. Ultimately, language technology should serve speech communities and their language varieties, and not the other way round. We need to identify new ways of working that are centered on speech communities and their varieties—what we refer to as speaker-centric approach. In this section, we provide recommendations and opportunities towards speaker-centric work that foresees active engagement with speech communities.

Before starting to engage with speech communities, it is advisable to become aware that local language varieties may be perceived very differently by their own speakers. Local languages and dialects of Italy have been historically subjected to prejudices and censorship. This culminated with the Italianization policy implemented by the fascist regime in 1923–1942 whereby “[local language varieties] were banned in the most absolute way [...] even when playing with classmates” (Camilleri and De Mauro, 2014), teaching in languages other than Italian was abolished, and foreign toponyms and surnames were changed to Italian-sounding forms. Among other things, this contributed over the next half century to the continued view of local language varieties as a “synonym of ignorance and lack of integration” (D’Agostino, 2015). Recent years have instead witnessed an overall change in attitude on the matter, especially by the young, for whom local varieties are rather rediscovered as an additional expressive resource in their communicative reportoire (Berruto, 2006). It is therefore necessary to realize in advance that—even within the same community—we may encounter speakers with diverse sensitivities and motivations, and that those may also be influenced by political parties that leverage language varieties for independence purposes. We need to remember that speech communities do not have a “single voice” (Bird, 2020) and that language ideologies and practices may change and be embraced differently over time (e.g., Griko in Pellegrino, 2021).

Building relationships with speech communities (Liu et al., 2022; Schwartz, 2022; Bird, 2020) is pivotal for speaker-centric work. It allows researchers not only to get a better sense of local communities’ attitudes and aspirations, and understand the individual linguistic and socio-political contexts at the micro-level, but also to learn about local agendas to support language vitality. However, the engagement should not be for the sole benefit of the researcher, but rather based on equity, reciprocity, and respect (Bird, 2020). From here naturally comes mutual trust, deep understanding of community needs, and thus opportunities for locally meaningful language technology applications—that may range from online dictionaries, to computer-assisted language education, to multilingual information access, depending on the individual situation.²² In the context of Italy, it is important to note that the engagement process and involved actors may differ across communities. For instance, very small communities in which language varieties are mostly spoken by elders (e.g., Cimbrian, Calabrian Greek; cf. Table 1) are represented by a number of cultural institutes that occasionally promote initiatives on language and culture. Participating to local events, understanding customs and traditions, and ask curiosity-driven questions is probably the only way to start building meaningful bonds in this space.²³ Instead, larger speech communities of non-officially recognized varieties (e.g., Neapolitan, Venetian; cf. Table 1) are often supported by politically-polarized bodies, but language varieties are spoken even by younger generations (ISTAT, 2017). It is advisable here to engage with individuals with diverse backgrounds and demographic characteristics. Given the number of speakers of those varieties, if casual relationships are not already in place, bonds can be easily established in the most diverse environments, including academia. Once a collaboration space between communities and NLP researchers is found, the involvement of speech communities must not end. In the speaker-centric approach, communities are involved at multiple stages of the design process, inspired by participatory design methods (Caselli et al., 2021). External language technologists need to recall that they work with others’ data for supporting vitality of others’ language varieties, and that only speech communities can reliably judge the usefulness and representativeness of a given technological artifact, both during and after the process. About representativeness, it is important to acknowledge that language and culture are inseparable, and that current NLP is not culturally sensitive (Hershcovich et al., 2022). Shared knowledge may differ from place to place, and this indeed shapes language. It would not be surprising if a machine translation system for Cimbrian—assuming that this is actually needed—homogenized “snow” to a single word, regardless of the many names it gets in Cimbrian highlands according to seasons and conditions (Rigoni Stern, 1998). Broadly, this is a motivation for NLP to start shifting from the traditional, monocultural view of language to a more inclusive, culturally-aware language technology. Moreover, it opens opportunities at the intersection of participatory design and NLP, e.g., new evaluation methods based on continuous communities’ feedback.

Responsibly supporting the vitality of language varieties of Italy by adopting a speaker-centric approach could be a difficult process to initiate. Moreover, in pursing this goal we may find it valuable to build concrete relationships with other stakeholders, exchange local knowledge and experience, and establish collaborations across speech communities (e.g., those sharing similar aspirations or which language varieties are closely related) and researchers from different academic disciplines (e.g., NLP, linguistics, anthropology). To ease this process, we initiated Varieties of the Boot,²⁴ a community aimed at responsibly supporting the vitality of language varieties of Italy by i) offering guidance on the speaker-centric approach to individuals interested in engaging in this space, ii) fostering discussion on practices that have been adopted in the past in diverse environments, lessons learnt, and mistakes to be avoided, and iii) encouraging participatory work between diverse speech communities, cultural institutes, and fields of study. Finally, iv) the community intends to serve as a reference point for actively raising awareness among the Italian community at large and external researchers about the often overlooked linguistic heritage of Italy. Practically, this may not only include scientific events such as thematic workshops, but also local events and communication activities on social media. The community opens valuable opportunities for stakeholders to learn from diverse perspectives, to responsibly engage with speech communities at different places, and to start participatory, interdisciplinary and intercultural collaborations.

There are many opportunities for NLP in neighboring areas. Language technology has traditionally focused on Standard Italian, but in everyday communication Italian speakers are instead used to use their own form of regional Italian (Avolio, 2009) (Section 2.3), i.e., varieties resulting from the geographical differentiation of the standard language. Ultimately, NLP should better represent the actual use of the Italian language. This also opens opportunities to study fairness of current NLP models across regional variants. Moreover, NLP to study language variation and contact at scale (Ramponi and Casula, 2023; Hovy and Purschke, 2018; Donoso and Sánchez, 2017, inter alia) can help in documenting how regional Italian varies across space. This can ultimately enrich and complement existing linguistic atlases such as ALI (Bartoli et al., 1995) and AIS (Jaberg et al., 1987). Finally, based on the actual use of Italy’s varieties, studying code-switching with a focus on its linguistic and social context (Doğruöz et al., 2021) may contribute to understanding language replacement processes (Cerruti and Regis, 2005).

In this work, we present the complex linguistic landscape of Italy, shedding light on the main assumptions and shortcomings of the default, machine-centric NLP approach for local language varieties. We advocate for a shift in the paradigm towards speaker-centric NLP, and provide recommendations and opportunities for responsible, participatory work aimed to support vitality of language varieties of Italy, designed with speech communities, for serving speakers and their needs.

We would like to thank the action editor and the anonymous reviewers for their insightful and constructive feedback during the review process. We would also like to thank Sara Tonelli and Barbara Plank for their advice on earlier versions of this paper, and the members of the Digital Humanities group at Fondazione Bruno Kessler for the precious conversations and contribution to data auditing. Further, we are grateful to Camilla Amendola for her valuable assistance in designing Figure 1.